Determinants of pancreatic cancer and malignant melanoma phenotypes in CDKN2A hereditary kindreds

CDKN2A 遗传家族中胰腺癌和恶性黑色素瘤表型的决定因素

基本信息

批准号：
9978727
负责人：
Martin Ernesto Fernandez-Zapico
金额：
$ 59.06万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-17 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9978727
关键词：
Address Affect Age Age of Onset Algorithms Alleles Alzheimer&apos s disease brain Back Biological Biology Brain Neoplasms CDKN2A gene Cancer Gene Mutation Cancer-Predisposing Gene Cell Cycle Progression Cells Code Communities Computer Models Cyclin-Dependent Kinase Inhibitor 2A DNA Data Data Set Development Diagnosis Discrimination Disease Environmental Exposure Etiology Gene Expression Gene Frequency Gene Mutation Gene-Modified Generations Genes Genetic Genome Genotype Germ-Line Mutation In Vitro Individual Inherited Knowledge Light Malignant Neoplasms Malignant neoplasm of pancreas Measures Methodology Methods Minor Modeling Molecular Mutation Oncogenic Pathway interactions Pattern Penetrance Performance Phenotype Process Proteins Proteomics Risk Signal Transduction Smoking Sun Exposure Syndrome Testing Training Transforming Growth Factor beta Variant WNT Signaling Pathway Weight base bead chip cancer genome cell growth convolutional neural network deep learning deep learning algorithm disease phenotype epidemiologic data epigenomics genetic analysis genetic variant genome-wide genome-wide analysis high dimensionality in vivo in vivo Model innovation insight interest kindred leukemia melanoma member metaplastic cell transformation mutant mutation carrier network models non-genetic novel novel diagnostics novel therapeutic intervention overexpression protein expression receptor tool transcriptome sequencing transmission process tumorigenesis

项目摘要

This proposal addresses Provocative Question #2. We will use innovative approaches to investigate how CDKN2A (which encodes p16) mutation carriers develop different cancer phenotypes (pancreatic cancer vs melanoma vs no cancer), and include both genetic and non-genetic factors. We have identified 4 large, multi- generational kindreds with a founder CDKN2A deleterious mutation (L16R, 47T>G). Our preliminary observations demonstrate that this mutant has lower expression and decreased ability to regulate cell cycle progression compared to wild type protein. Our sequencing studies of kindred members with different cancer phenotypes have identified potential variants in novel genes that modify risk (LGR6, a co-receptor of Wnt signaling and COL11A1, which participates in oncogenic signaling, including TGFbeta). We will determine the ability of the p16 mutant to promote transformation and how it is influenced by interaction with the above candidate modifier genes, LGR6 or COL11A1, in pancreatic cancer and melanoma. We will also develop novel computational models using machine deep learning, to generate networks that capture high dimensional features to integrate gene, biology, and cancer phenotype. This approach will be extended to kindreds with other CDKN2A mutations. Our Specific Aims are to: (1) Identify genotypes of potential modifier genes in multiple kindreds that feature pancreatic cancer and melanoma and known to carry CDKN2A germline mutations. We will use genome wide variant coverage of germline DNA from CDKN2A carriers from the 4 large L16R kindreds, plus additional members in 42 other similar CDKN2A kindreds. We will identify candidate modifier genes in the kindreds by rule-based statistical genetic analysis of genotypes. (2) Define the impact of CDKN2A L16R mutation on the function of p16 and its interplay with candidate modifier genes. We will elucidate the biological significance of mutations in CDKN2A and candidate modifier genes using functional and high throughput methodologies by analyzing the mechanism underlying the interplay between p16 and modifier genes; define new pathways cooperating with this interplay using a combination of genome wide studies to assess transformation in cells carrying p16 mutant or wild-type background using well established in vitro and in vivo models. (3) Develop a deep learning network model to integrate genetic, biological and epidemiological data to accurately infer pancreatic cancer and melanoma phenotypes and age of onset in mutation carriers. We will apply a convolutional neural network, a deep learning algorithm in the training dataset, develop a back-propagation algorithm to fine tune “weights,” and construct mutation-gene networks to capture high-dimensional features for each disease subclass. We will acquire and disseminate new knowledge and tools to the scientific community. Our integrated methods and approach will bring insight into how different cancer phenotypes can occur with identical predisposing mutations, which can be applied to other cancer syndromes with similar challenges.

该提案解决了挑衅性问题2。我们将使用创新的方法来研究 CDKN2A（编码p16）突变载体发展了不同的癌症表型（胰腺癌与黑色素瘤与无癌症），包括遗传因素和非遗传因素。我们已经确定了4个大型，多具有创始人CDKN2A有害突变（L16R，47T> g）的世代亲戚。我们的初步观察结果表明，该突变体具有较低的表达和提高的调节细胞周期的能力与野生型蛋白相比进展。我们对不同癌症的亲属成员的测序研究表型已经确定了改变风险的新基因中的潜在变异（LGR6，Wnt的共受体。信号传导和Col11a1，参与包括TGFBETA在内的致癌信号传导。我们将确定 p16突变体促进转化及其与上述相互作用的影响胰腺癌和黑色素瘤中的候选修饰剂基因，LGR6或COL11A1。我们还将开发小说使用机器深学习的计算模型，生成捕获高维的网络整合基因，生物学和癌症表型的特征。这种方法将扩展到与其他CDKN2A突变。我们的具体目的是：（1）确定潜在修饰基因的基因型多种具有胰腺癌和黑色素瘤并携带CDKN2A种系的亲属突变。我们将使用来自4个大型的CDKN2A载体的种系DNA的基因组宽变体覆盖率 L16R Kindreds，以及其他42位类似的CDKN2A Kindreds中的其他成员。我们将确定候选人基于基因型的基于规则的统计遗传分析，在Kindred中进行了修饰基因。（2）定义 p16功能及其与候选修饰符基因的相互作用的CDKN2A L16R突变。我们将使用功能和高吞吐量方法通过分析p16和修饰符之间相互作用的基础机制基因；使用基因组广泛研究的组合来定义与此相互作用合作的新途径评估使用良好的体外和In中建立的携带p16突变体或野生型背景的细胞转化体内模型。（3）开发一个深度学习网络模型，以整合遗传，生物学和流行病学数据准确推断胰腺癌和黑色素瘤表型和发作年龄在突变载体中。我们将应用一个卷积神经网络，这是一种培训中的深度学习算法数据集，开发一种反向传播算法以微调“权重”，然后构建突变基网络捕获每个疾病亚类的高维特征。我们将获取并传播新知识和科学界的工具。我们的综合方法和方法将使您洞悉不同癌症表型可能会随着相同的易感突变而发生，可以应用于其他癌症具有类似挑战的综合征。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Ribosomal proteins regulate 2-cell-stage transcriptome in mouse embryonic stem cells.

DOI：
10.1016/j.stemcr.2022.12.007
发表时间：
2023-02-14
期刊：
STEM CELL REPORTS
影响因子：
5.9
作者：
Yi, Yao;Zeng, Yingying;Sam, Tsz Wing;Hamashima, Kiyofumi;Tan, Rachel Jun Rou;Warrier, Tushar;Phua, Jun Xiang;Taneja, Reshma;Liou, Yih-Cherng;Li, Hu;Xu, Jian;Loh, Yuin-Han
通讯作者：
Loh, Yuin-Han